Program Coordinator of AI Safety Camp.
Great overview! I find this helpful.
Next to intrinsic optimisation daemons that arise through training internal to hardware, suggest adding extrinsic optimising "divergent ecosystems" that arise through deployment and gradual co-option of (phenotypic) functionality within the larger outside world.
AI Safety so far research has focussed more on internal code (particularly CS/ML researchers) computed deterministically (within known statespaces, as mathematicians like to represent). That is, rather than complex external feedback loops that are uncomputable – given Good Regulator Theorem limits and the inherent noise interference on signals propagating through the environment (as would be intuitive for some biologists and non-linear dynamics theorists).
So extrinsic optimisation is easier for researchers in our community to overlook. See this related paper by a physicist studying origins of life.
Unfortunately, perhaps due to the prior actions of others in your same social group, a deceptive frame of interpretation is more likely to be encountered first, effectively 'inoculating' everyone else in the group against an unbiased receipt of any further information.
Written in 2015. Still relevant.
Say maybe Illusion of Truth and Ambiguity Effect each are biasing how researchers in AI Safety evaluate one option below.
If you had to choose, which bias would more likely apply to which option?
it needs to plug into the mathematical formalizations one would use to do the social science form of this.
Could you clarify what you mean with a "social science form" of a mathematical formalisation?
I'm not familiar with this.
they're right to look at people funny even if they have the systems programming experience or what have you.
It was expected and understandable that people look funny at the writings from a multi-skilled researcher with new ideas that those people were not yet familiar with.
Let's move on from first impressions.
If with simulation, we can refer to a model that is computed to estimate a factor on which further logical deduction steps are based on, that would connect up with Forrest's work (it's not really about multi-agent simulation though).
Based on what I learned from Forrest, we need to distinguish the 'estimation' factors from the 'logical entailment' factors. That the notion of "proof" is only with respect to that which can be logically entailed. Everything else is about assessment. In each case, we need to be sure we are doing the modelling correctly.
For example, it could be argued that step 'b' below is about logical entailment, though according to Forrest most would argue that it is an assessment. Given that it depends on both physics and logic (via comp-sci modeling), it depends on how one regards the notion of 'observation', and where that is empirical or analytic observation.
- b; If AGI/APS is permitted to continue to exist,
then it will inevitably, inexorably,
implement and manifest certain convergent behaviors.
- c; that among these inherent convergent behaviors
will be at least all of:.
- 1; to/towards self existence continuance promotion.
- 2; to/towards capability building capability,
a increase seeking capability,
a capability of seeking increase,
capability/power/influence increase, etc.
- 3; to/towards shifting
ambient environmental conditions/context
to/towards favoring the production of
(variants of, increases of)
its artificial substrate matrix.
Note again: the above is not formal reasoning. It is a super-short description of what two formal reasoning steps would cover.
Really appreciate you sharing your honest thoughts her, Rekrul.
From my side, I’d value actually discussing the reasoning forms and steps we already started to outline on the forum. For example, the relevance of intrinsic vs extrinsic selection and correction, or the relevance of the organic vs. artificial substrate distinction. These distinctions are something I would love to openly chat about with you (not the formal reasoning – I’m the bridge-builder, Forrest is the theorist).
That might feel unsatisfactory – in the sense of “why don’t you just give us the proof now?”
As far as I can tell (Forrest can correct me later), there are at least two key reasons:
There is a tendency amongst AI Safety researchers to want to cut to the chase to judging the believability of the conclusion itself. For example, notice that I tried to clarify several argument parts in comment exchanges with Paul, with little or no response. People tend to believe that this would be the same as judging a maths proof over idealised deterministic and countable spaces. Yet formal reasoning here would have have to reference and build up premises from physical theory in indeterministic settings. So we actually need to clarify how a different form of formal reasoning is required here, that does not look like what would be required for P=NP. Patience is needed on the side of our interlocutors.
While Forrest does have most of the argument parts formalised, his use of precise analytical language and premises are not going to be clear to you. Mathematicians are not the only people who use formal language and reasoning steps to prove impossibilities by contradiction. Some analytical philosophers do too (as do formal verification researchers in industrial software engineering using different notation for logic transformation, etc.). No amount of “just give the proof to us and leave it to us to judge” lends us confidence that the judging would track the reasoning steps – if those people already did not track correspondences of some first basic argument parts described by the explanatory writings by Forrest or I that their comments referred to. Even if they are an accomplished mathematician, they are not going to grasp the argumentation if they skim through the text, judging it based on their preconception of what language the terms should be described in or how the formal reasoning should be structured.
I get that people are busy, but this is how it is. We are actually putting a lot of effort and time into communication (and are very happy to get your feedback on that!). And to make this work, they (or others) will need to put in commensurate effort on their end. It is up to them to show that they are not making inconsistent jumps in reasoning there, or talking in terms of their intuitive probability predictions about the believability of the end result, where we should be talking about binary logic transformations.
And actually, such nitty-gritty conversations would be really helpful for us too! Here is what I wrote before in response to another person’s question whether a public proof is available:
Main bottleneck is (re)writing it in a language that AI(S) researchers will understand without having to do a lot of reading/digging in the definitions of terms and descriptions of axioms/premises. A safety impossibility theorem can be constructed from various forms that are either isomorphic with others or are using separate arguments (eg. different theoretical limits covering different scopes of AGI interaction) to arrive at what seems to be an overdetermined conclusion (that long-term AGI safety is not possible).
We don't want to write it out so long that most/all readers drop out before they get to parse through the key reasoning steps. But we also do not want to make it so brief and dense that researchers are confused about at what level of generality we're talking about, have to read through other referenced literature to understand definitions, etc.
Also, one person (a grant investigator) has warned us that AI safety researchers would be too motivated against the conclusion (see 'belief bias') that few would actually attempt to read through a formal safety impossibility theorem. That's indeed likely based on my exchanges so far with AIS researchers (many of them past organisers or participants of AISC). So that is basically why we are first writing a condensed summary (for the Alignment Forum and beyond) that orders the main arguments for long-term AGI safety impossibility without precisely describing all axioms and definitions of terms used, covering all the reasoning gaps to ensure logical consistency, etc.
Note: Forrest has a background in analytical philosophy; he does not write in mathematical notation. Another grant investigator we called with had the expectation that the formal reasoning is necessarily written out in mathematical notation (a rough post-call write-up consolidating our impressions and responses to that conversation): https://mflb.com/ai_alignment_1/math_expectations_psr.html
Also note that Forrest’s formal reasoning work got funded by a $170K grant by Survival and Flourishing Fund. So some grant investigators were willing to bet on this work with money.
One thing Paul talks about constantly is how useful it would be if he had some hard evidence a current approach is doomed, as it would allow the community to pivot. A proof of alignment impossibility would probably make him ecstatic if it was correct (even if it puts us in quite a scary position).
I respect this take then by Paul a lot. This is how I also started to think about it a year ago.
BTW, I prefer you being blunt, so glad you’re doing that.
A little more effort to try to understand where we could be coming from would be appreciated. Particularly given what’s at stake here – a full extinction event.
Neither Forrest nor I have any motivation to post unsubstantiated claims. Forrest because frankly, he does not care one bit about being recognised by this community – he just wants to find individuals who actually care enough to consider the arguments rigorously. Me because all I’d be doing is putting my career at risk.
You can't complain about people engaging with things other than your idea if the only thing they can even engage with is your idea.
The tricky thing here is that a few people are reacting by misinterpreting the basic form of the formal reasoning at the onset, and judging the merit of the work by their subjective social heuristics.
Which does not lend me (nor Forrest) confidence that those people would do a careful job at checking the term definitions and reasoning steps – particularly if written in precise analytic language that is unlike the mathematical notation they’re used to.
The filter goes both ways.
Instead you have decided to make this post and trigger more crank alarms.
Actually, this post was written in 2015 and I planned last week to reformat it and post it. Rereading it, I’m just surprised how well it appears to line up with the reactions.
The problem of a very poor signal to noise ratio from messages received from people outside of the established professional group basically means that the risk of discarding a good proposal from anyone regarded as an outsider is especially likely.
This insight feels relevant to a comment exchange I was in yesterday. An AI Safety insider (Christiano) lightly read an overview of work by an outsider (Landry). The insider then judged the work to be "crankery", in effect acting as a protecting barrier against other insiders having to consider the new ideas.
The sticking point was the claim "It is 100% possible to know that X is 100% impossible", where X is a perpetual motion machine or a 'perpetual general benefit machine' (ie. long-term safe and beneficial AGI).
The insider believed this was an exaggerated claim, which meant we first needed to clarify epistemics and social heuristics, rather than the substantive argument form. The reactions by the busy "expert" insider, who had elected to judge the formal reasoning, led to us losing trust that they would proceed in a patient and discerning manner.
There was simply not enough common background and shared conceptual language for the insider to accurately interpret the outsider's writings ("very poor signal to noise ratio from messages received").
Add to that:
I mean, someone recognised as an expert in AI Safety could consciously mean well trying to judge an outsider's work accurately – in the time they have. But that's a lot of biases to counteract.
Forrest actually clarified the claim further to me by message:
Re "100%" or "fully knowable":
By this, I usually mean that the analytic part of an argument is fully finite and discrete, and that all parts (statements) are there, the transforms are enumerated, known to be correct etc (ie, is valid).
In regards to the soundness aspect, that there is some sort of "finality" or "completeness" in the definitions, such that I do not expect that they would ever need to be revised (ie, is at once addressing all necessary aspects, sufficiently, and comprehensively), and that the observations are fully structured by the definitions, etc. Usually this only works for fairly low level concepts, things that track fairly closely to the theory of epistemology itself -- ie, matters of physics that involve symmetry or continuity directly (comparison) or are expressed purely in terms of causation, etc.
One good way to test the overall notion is that something is "fully 100% knowable" if one can convert it to a computer program, and the program compiles and works correctly. The deterministic logic of computers cannot be fooled, as people sometimes can, as there is no bias. This is may be regarded by some as a somewhat high standard, but it makes sense of me as it is of the appropriate type: Ie, a discrete finite result being tested in a purely discrete finite environment. Hence, nothing missing can hide.
But the point is – few readers will seriously consider this message.
That's my experience, sadly.
The common reaction I noticed too from talking with others in AI Safety is that they immediately devaluated that extreme-sounding conclusion that is based on the research of an outsider. A conclusion that goes against their prior beliefs, and against their role in the community.
Your remarks make complete sense.
Forest mentioned that for most people, reading his precise "EGS" format will be unparsable unless one has had practice with it. Also agreed that there is no background or context. The "ABSTract" is really too often too brief a note, usually just a reminder what the overall idea is. And the text itself IS internal notes, as you have said.
He says that it is a good reminder that he should remember to convert "EGS" to normal prose before publishing. He does not always have the energy or time or enthusiasm to do it. Often it requires a lot of expansion too – ie, some writing has to expand to 5 times their "EGS" size.
I'll also work on this! There's a lot of content to share, but will try and format and rephrase to be better followable for readers on LessWrong.
The premise that “infinite value” is possible, is an assumption.
This seems a bit like the presumption that “divide by zero” is possible. Assigning a probability to the possibility that divide by zero results in a value doesn’t make sense, I think, because the logical rules themselves rules this out.
However, if I look at this together with your earlier post (http://web.archive.org/web/20230317162246/https://www.lesswrong.com/posts/dPCpHZmGzc9abvAdi/orthogonality-thesis-is-wrong): I think I get where you’re coming from in that if the agent can conceptualise that (many) (extreme) high-value states are possible where those values are not yet known to it, yet still plans for those value possibilities in some kind of “RL discovery process”, then internal state-value optimisation converges on power-seeking behaviour — as optimal for reaching the expected value of such states in the future (this further assumes that the agent’s prior distribution lines up – eg. assumes unknown positive values are possible, does not have a prior distribution that is hugely negatively skewed over negative rewards).
I think initially specifying premises such as these more precisely initially ensures the reasoning from there is consistent/valid. The above would not apply to any agent, nor even to any “AGI” (a fuzzy term; I would define it more specifically as “fully-autonomous, cross-domain-optimising, artificial machinery”